标题： 基于参数嵌入的物理驱动神经网络，从加权图像生成定量MRI图谱 显示英文标题

标题： A Physics-Driven Neural Network with Parameter Embedding for Generating Quantitative MR Maps from Weighted Images

摘要： 我们提出了一种基于深度学习的方法，将MRI序列参数整合起来，以提高从临床加权MRI进行定量图像合成的准确性与泛化能力。 我们的物理驱动神经网络通过参数嵌入直接将MRI序列参数——重复时间（TR）、回波时间（TE）和反转时间（TI）——嵌入到模型中，使网络能够学习MRI信号形成的潜在物理原理。 该模型以传统的T1加权、T2加权和T2-FLAIR图像作为输入，并合成T1、T2和质子密度（PD）的定量图。 在健康大脑MRI图像上进行训练后，在内部和外部测试数据集上进行了评估。 所提出的方法在所有合成参数图上实现了高性能，PSNR值超过34 dB，SSIM值高于0.92。 它在准确性和鲁棒性方面优于传统深度学习模型，包括对之前未见过的大脑结构和病变数据。 值得注意的是，我们的模型准确地合成了这些未见过的病理区域的定量图，突显了其优越的泛化能力。 通过参数嵌入整合MRI序列参数，使神经网络能够更好地学习MR信号的物理特性，显著提高了定量MRI合成的性能和可靠性。 这种方法在加速qMRI和提高其临床实用性方面显示出巨大的潜力。 显示英文摘要

摘要： We propose a deep learning-based approach that integrates MRI sequence parameters to improve the accuracy and generalizability of quantitative image synthesis from clinical weighted MRI. Our physics-driven neural network embeds MRI sequence parameters -- repetition time (TR), echo time (TE), and inversion time (TI) -- directly into the model via parameter embedding, enabling the network to learn the underlying physical principles of MRI signal formation. The model takes conventional T1-weighted, T2-weighted, and T2-FLAIR images as input and synthesizes T1, T2, and proton density (PD) quantitative maps. Trained on healthy brain MR images, it was evaluated on both internal and external test datasets. The proposed method achieved high performance with PSNR values exceeding 34 dB and SSIM values above 0.92 for all synthesized parameter maps. It outperformed conventional deep learning models in accuracy and robustness, including data with previously unseen brain structures and lesions. Notably, our model accurately synthesized quantitative maps for these unseen pathological regions, highlighting its superior generalization capability. Incorporating MRI sequence parameters via parameter embedding allows the neural network to better learn the physical characteristics of MR signals, significantly enhancing the performance and reliability of quantitative MRI synthesis. This method shows great potential for accelerating qMRI and improving its clinical utility.